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Updated: Jun 13, 2025

A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports
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一个轻量级图形神经网络来预测冠状动脉疾病患者的长期死亡率:一种可解释的因果关系意识方法

Mohammad Yaseliani1, Md Noor-E-Alam2, Osama Dasa3

  • 1Department of Pharmaceutical Outcomes and Policy, College of Pharmacy, University of Florida, Gainesville, FL, USA.

Journal of biomedical informatics
|May 13, 2025
PubMed
概括

本研究引入了一种轻量级图形神经网络 (GNN) 模型,用于预测冠状动脉疾病 (CAD) 死亡率. 开发的系统CAD-SS准确识别高风险患者,改善心血管疾病的临床决策.

关键词:
有因果的人工智能 (Causal AI)冠状动脉疾病 (CAD) 是一种冠状动脉疾病.深度学习 (DL) 是指深度学习.图形神经网络 (GNN) 是一个神经网络.机器学习 (ML) 是指机器学习.倾向性得分匹配 (PSM) 是一种方法.

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